Introduction: LIH Department DCR

Here are preliminary results of the bibliometric mapping of the 2022 Luxembourg research evaluation. Its purpose is:

The method for the research-field-mapping can be reviewed here:

Rakas, M., & Hain, D. S. (2019). The state of innovation system research: What happens beneath the surface?. Research Policy, 48(9), 103787.

Seed Articles

The seed articles deemed representative for the active areas of research in the institution, and include authors affiliated with the institution. They can be selected in three ways:

  1. Via bibliographic clustering of the institutions publications and selection of most central articles per cluster (only clsuters where n >= 0.05N). Selection can be found at: https://github.com/daniel-hain/biblio_lux_2022/blob/master/output/seed/scopus_lih_dcr_seed.csv
  2. Manual selection of relevant publications.
  3. A combination of 1. and 2.

The present analysis is based on the following seed articles:

AU PY TI JI
OUDIN A;BAUS V;BARTHELEMY V… 2021 PROTOCOL FOR DERIVATION OF ORGANOIDS AND PATIENT-DERIVED ORTHOTOPIC XENOGRAFTS FROM GLIOMA PATIEN… STAR. PROTOC.
OIZEL K;TAIT-MULDER J;FERNA… 2020 FORMATE INDUCES A METABOLIC SWITCH IN NUCLEOTIDE AND ENERGY METABOLISM CELL DEATH DIS.
BIOLATO AM;FILALI L;WURZER … 2020 ACTIN REMODELING AND VESICULAR TRAFFICKING AT THE TUMOR CELL SIDE OF THE IMMUNOLOGICAL SYNAPSE DI… INT. REV. CELL MOL. BIOL.
BRETSCHER C;MARCHINI A 2019 H-1 PARVOVIRUS AS A CANCER-KILLING AGENT: PAST, PRESENT, AND FUTURE VIRUSES
GARGIULO E;PAGGETTI J;MOUSS… 2019 HEMATOLOGICAL MALIGNANCY-DERIVED SMALL EXTRACELLULAR VESICLES AND TUMOR MICROENVIRONMENT: THE ART… CELLS
ESKILSSON E;RØSLAND GV;SOLE… 2018 EGFR HETEROGENEITY AND IMPLICATIONS FOR THERAPEUTIC INTERVENTION IN GLIOBLASTOMA NEURO-ONCOLOGY
JANJI B;BERCHEM G;CHOUAIB S 2018 TARGETING AUTOPHAGY IN THE TUMOR MICROENVIRONMENT: NEW CHALLENGES AND OPPORTUNITIES FOR REGULATIN… FRONT. IMMUNOL.
NOMAN MZ;VAN MOER K;MARANI … 2018 CD47 IS A DIRECT TARGET OF SNAI1 AND ZEB1 AND ITS BLOCKADE ACTIVATES THE PHAGOCYTOSIS OF BREAST C… ONCOIMMUNOLOGY
BAHLAWANE C;SCHMITZ M;LETEL… 2017 INSIGHTS INTO LIGAND STIMULATION EFFECTS ON GASTRO-INTESTINAL STROMAL TUMORS SIGNALLING CELL. SIGNAL.
BOURMAUD A;GALLIEN S;DOMON B 2016 PARALLEL REACTION MONITORING USING QUADRUPOLE-ORBITRAP MASS SPECTROMETER: PRINCIPLE AND APPLICATIONS PROTEOMICS

Topic modelling

Here, we report the results of a LDA topic-modelling (basically, clustering on words) on all title+abstract texts.

Topics by topwords

Note: While this static vies is helpful, I recommend using the interactive LDAVis version to be found under https://daniel-hain.github.io/biblio_lux_2022/output/topic_modelling/LDAviz_lih_dcr.rds/index.html#topic=1&lambda=0.60&term=. For functionality and usage, see technical description in the next tab.

Topics over time

Technical Description

LDA Topic Modelling

Topic modeling is a type of statistical modeling for discovering the abstract “topics” that occur in a collection of documents. Latent Dirichlet Allocation (LDA, Blei et al., 2003) is an example of topic model and is used to classify text in a document to a particular topic.

LDA is a generative probabilistic model that assumes each topic is a mixture over an underlying set of words, and each document is a mixture of over a set of topic probabilities. It builds a topic per document model and words per topic model, modeled as Dirichlet distributions.

LDAVis

LDAvis is a web-based interactive visualisation of topics estimated using LDA (Sievert & Shirley, 2014). It provides a global view of the topics (and how they differ from each other), while at the same time allowing for a deep inspection of the terms most highly associated with each individual topic. The package extracts information from a fitted LDA topic model to inform an interactive web-based visualization. The visualisation has two basic pieces.

The left panel visualise the topics as circles in the two-dimensional plane whose centres are determined by computing the Jensen–Shannon divergence between topics, and then by using multidimensional scaling to project the inter-topic distances onto two dimensions. Each topic’s overall prevalence is encoded using the areas of the circles.

The right panel depicts a horizontal bar chart whose bars represent the individual terms that are the most useful for interpreting the currently selected topic on the left. A pair of overlaid bars represent both the corpus-wide frequency of a given term as well as the topic-specific frequency of the term.

The \(\lambda\) slider allows to rank the terms according to term relevance. By default, the terms of a topic are ranked in decreasing order according their topic-specific probability ( \(\lambda\) = 1 ). Moving the slider allows to adjust the rank of terms based on much discriminatory (or “relevant”) are for the specific topic. The suggested optimal value of \(\lambda\) is 0.6.

Knowledge Bases: Co-Citation network analysis

Note: This analysis refers the co-citation analysis, where the cited references and not the original publications are the unit of analysis. See tab Technical descriptionfor additional explanations

Knowledge Bases summary

In order to partition networks into components or clusters, we deploy a community detection technique based on the Lovain Algorithm (Blondel et al., 2008). The Lovain Algorithm is a heuristic method that attempts to optimize the modularity of communities within a network by maximizing within- and minimizing between-community connectivity. We identify the following communities = knowledge bases.

name dgr_int dgr
Knowledge Base 1: KB 1: unlabeled (n = 4248, density =3.96)
RAPOSO G. STOORVOGEL W. EXTRACELLULAR VESICLES: EXOSOMES MICROVESICLES AND FRIENDS (2013) 16828 17297
VALADI H. EKSTROM K. BOSSIOS A. SJOSTRAND M. LEE J.J. LOTVALL J.O. EXOSOME-MEDIATED TRANSFER OF MRNAS AND MICRORNAS IS A NOVEL MECHANISM OF GENETIC… 9851 10011
TRAJKOVIC K. HSU C. CHIANTIA S. RAJENDRAN L. WENZEL D. WIELAND F. SCHWILLE P. SIMONS M. CERAMIDE TRIGGERS BUDDING OF EXOSOME VESICLES INTO MULTIVES… 8374 8446
VALADI H. EKSTRÖM K. BOSSIOS A. SJÖSTRAND M. LEE J.J. LÖTVALL J.O. EXOSOME-MEDIATED TRANSFER OF MRNAS AND MICRORNAS IS A NOVEL MECHANISM OF GENETIC… 7018 7106
COLOMBO M. RAPOSO G. THERY C. BIOGENESIS SECRETION AND INTERCELLULAR INTERACTIONS OF EXOSOMES AND OTHER EXTRACELLULAR VESICLES (2014) 6392 6559
TAYLOR D.D. GERCEL-TAYLOR C. MICRORNA SIGNATURES OF TUMOR-DERIVED EXOSOMES AS DIAGNOSTIC BIOMARKERS OF OVARIAN CANCER (2008) 6004 6078
RAPOSO G. NIJMAN H.W. STOORVOGEL W. LIEJENDEKKER R. HARDING C.V. MELIEF C.J. GEUZE H.J. B LYMPHOCYTES SECRETE ANTIGEN-PRESENTING VESICLES (1996) 5666 5723
JOHNSTONE R.M. ADAM M. HAMMOND J.R. ORR L. TURBIDE C. VESICLE FORMATION DURING RETICULOCYTE MATURATION. ASSOCIATION OF PLASMA MEMBRANE ACTIVITIES W… 5486 5543
ROBBINS P.D. MORELLI A.E. REGULATION OF IMMUNE RESPONSES BY EXTRACELLULAR VESICLES (2014) 4867 5016
HOOD J.L. SAN R.S. WICKLINE S.A. EXOSOMES RELEASED BY MELANOMA CELLS PREPARE SENTINEL LYMPH NODES FOR TUMOR METASTASIS (2011) 4790 4935
Knowledge Base 2: KB 2: unlabeled (n = 2439, density =2.3)
COX J. MANN M. MAXQUANT ENABLES HIGH PEPTIDE IDENTIFICATION RATES INDIVIDUALIZED P.P.B.-RANGE MASS ACCURACIES AND PROTEOME-WIDE PROTEIN QUANTIFICAT… 9462 9646
COX J. NEUHAUSER N. MICHALSKI A. SCHELTEMA R.A. OLSEN J.V. MANN M. ANDROMEDA: A PEPTIDE SEARCH ENGINE INTEGRATED INTO THE MAXQUANT ENVIRONMENT (2011) 3788 3809
COX J. HEIN M.Y. LUBER C.A. PARON I. NAGARAJ N. MANN M. ACCURATE PROTEOME-WIDE LABEL-FREE QUANTIFICATION BY DELAYED NORMALIZATION AND MAXIMAL PEPTI… 3567 3595
TYANOVA S. TEMU T. SINITCYN P. CARLSON A. HEIN M.Y. GEIGER T. MANN M. COX J. THE PERSEUS COMPUTATIONAL PLATFORM FOR COMPREHENSIVE ANALYSIS OF (PROTE) 2626 2642
RAPPSILBER J. MANN M. ISHIHAMA Y. PROTOCOL FOR MICRO-PURIFICATION ENRICHMENT PRE-FRACTIONATION AND STORAGE OF PEPTIDES FOR PROTEOMICS USING STAGETI… 2422 2446
PETERSON A.C. RUSSELL J.D. BAILEY D.J. WESTPHALL M.S. COON J.J. PARALLEL REACTION MONITORING FOR HIGH RESOLUTION AND HIGH MASS ACCURACY QUANTITATIV… 2231 2242
MACLEAN B. TOMAZELA D.M. SHULMAN N. CHAMBERS M. FINNEY G.L. FREWEN B. KERN R. MACCOSS M.J. SKYLINE: AN OPEN SOURCE DOCUMENT EDITOR FOR CREATING AND… 2183 2189
AEBERSOLD R. MANN M. MASS-SPECTROMETRIC EXPLORATION OF PROTEOME STRUCTURE AND FUNCTION (2016) 1808 1814
PICOTTI P. AEBERSOLD R. SELECTED REACTION MONITORING-BASED PROTEOMICS: WORKFLOWS POTENTIAL PITFALLS AND FUTURE DIRECTIONS (2012) 1498 1507
LANGE V. PICOTTI P. DOMON B. AEBERSOLD R. SELECTED REACTION MONITORING FOR QUANTITATIVE PROTEOMICS: A TUTORIAL (2008) 1427 1432
Knowledge Base 3: KB 3: unlabeled (n = 2220, density =1.74)
HANAHAN D. WEINBERG R.A. HALLMARKS OF CANCER: THE NEXT GENERATION (2011) 3354 9690
PARDOLL D.M. THE BLOCKADE OF IMMUNE CHECKPOINTS IN CANCER IMMUNOTHERAPY (2012) 1969 2544
SHARMA P. ALLISON J.P. THE FUTURE OF IMMUNE CHECKPOINT THERAPY (2015) 1553 1704
TOPALIAN S.L. DRAKE C.G. PARDOLL D.M. IMMUNE CHECKPOINT BLOCKADE: A COMMON DENOMINATOR APPROACH TO CANCER THERAPY (2015) 832 1099
WILSON W.R. HAY M.P. TARGETING HYPOXIA IN CANCER THERAPY (2011) 823 861
KEIR M.E. BUTTE M.J. FREEMAN G.J. SHARPE A.H. PD-1 AND ITS LIGANDS IN TOLERANCE AND IMMUNITY (2008) 816 867
BARSOUM I.B. SMALLWOOD C.A. SIEMENS D.R. GRAHAM C.H. A MECHANISM OF HYPOXIA-MEDIATED ESCAPE FROM ADAPTIVE IMMUNITY IN CANCER CELLS (2014) 714 729
CHEN J. JIANG C.C. JIN L. ZHANG X.D. REGULATION OF PD-L1: A NOVEL ROLE OF PRO-SURVIVAL SIGNALLING IN CANCER (2016) 674 674
QUAIL D.F. JOYCE J.A. MICROENVIRONMENTAL REGULATION OF TUMOR PROGRESSION AND METASTASIS (2013) 668 2138
ISHIDA Y. AGATA Y. SHIBAHARA K. HONJO T. INDUCED EXPRESSION OF PD-1 A NOVEL MEMBER OF THE IMMUNOGLOBULIN GENE SUPERFAMILY UPON PROGRAMMED CELL DEAT… 537 554
Knowledge Base 4: KB 4: unlabeled (n = 2142, density =3.64)
LEVY J.M.M. TOWERS C.G. THORBURN A. TARGETING AUTOPHAGY IN CANCER (2017) 3527 3622
WHITE E. DECONVOLUTING THE CONTEXT-DEPENDENT ROLE FOR AUTOPHAGY IN CANCER (2012) 2776 2841
WHITE E. THE ROLE FOR AUTOPHAGY IN CANCER (2015) 2414 2478
YUE Z. JIN S. YANG C. LEVINE A.J. HEINTZ N. BECLIN 1 AN AUTOPHAGY GENE ESSENTIAL FOR EARLY EMBRYONIC DEVELOPMENT IS A HAPLOINSUFFICIENT TUMOR SUPPR… 1886 1905
LIANG X.H. JACKSON S. SEAMAN M. BROWN K. KEMPKES B. HIBSHOOSH H. LEVINE B. INDUCTION OF AUTOPHAGY AND INHIBITION OF TUMORIGENESIS BY BECLIN 1 (1999) 1811 1826
AMARAVADI R. KIMMELMAN A.C. WHITE E. RECENT INSIGHTS INTO THE FUNCTION OF AUTOPHAGY IN CANCER (2016) 1786 1804
KIMMELMAN A.C. WHITE E. AUTOPHAGY AND TUMOR METABOLISM (2017) 1768 1791
KIM J. KUNDU M. VIOLLET B. GUAN K.L. AMPK AND MTOR REGULATE AUTOPHAGY THROUGH DIRECT PHOSPHORYLATION OF ULK1 (2011) 1762 1786
MIZUSHIMA N. KOMATSU M. AUTOPHAGY: RENOVATION OF CELLS AND TISSUES (2011) 1723 1748
MIZUSHIMA N. YOSHIMORI T. OHSUMI Y. THE ROLE OF ATG PROTEINS IN AUTOPHAGOSOME FORMATION (2011) 1570 1583
Knowledge Base 5: KB 5: unlabeled (n = 1498, density =3.18)
VERHAAK R.G. HOADLEY K.A. PURDOM E. WANG V. QI Y. WILKERSON M.D. MILLER C.R. MESIROV J.P. INTEGRATED GENOMIC ANALYSIS IDENTIFIES CLINICALLY RELEVAN… 2238 2327
COMPREHENSIVE GENOMIC CHARACTERIZATION DEFINES HUMAN GLIOBLASTOMA GENES AND CORE PATHWAYS (2008) 2146 2324
LOUIS D.N. PERRY A. REIFENBERGER G. VON DEIMLING A. FIGARELLA-BRANGER D. CAVENEE W.K. OHGAKI H. ELLISON D.W. THE 2016 WORLD HEALTH ORGANIZATION CLA… 1895 2030
PATEL A.P. TIROSH I. TROMBETTA J.J. SHALEK A.K. GILLESPIE S.M. WAKIMOTO H. CAHILL D.P. MARTUZA R.L. SINGLE-CELL RNA-SEQ HIGHLIGHTS INTRATUMORAL HET… 1589 1703
BRENNAN C.W. THE SOMATIC GENOMIC LANDSCAPE OF GLIOBLASTOMA (2013) 1338 1447
BRENNAN C.W. VERHAAK R.G. MCKENNA A. CAMPOS B. NOUSHMEHR H. SALAMA S.R. ZHENG S. BERMAN S.H. THE SOMATIC GENOMIC LANDSCAPE OF GLIOBLASTOMA (2013) 1211 1277
STUPP R. MASON W.P. VAN DEN BENT M.J. WELLER M. FISHER B. TAPHOORN M.J. BELANGER K. BOGDAHN U. RADIOTHERAPY PLUS CONCOMITANT AND ADJUVANT TEMOZOLOM… 1189 1282
VERHAAK R.G. INTEGRATED GENOMIC ANALYSIS IDENTIFIES CLINICALLY RELEVANT SUBTYPES OF GLIOBLASTOMA CHARACTERIZED BY ABNORMALITIES IN PDGFRA IDH1 EGFR… 1147 1234
PATEL A.P. SINGLE-CELL RNA-SEQ HIGHLIGHTS INTRATUMORAL HETEROGENEITY IN PRIMARY GLIOBLASTOMA (2014) 1141 1215
BAO S. WU Q. MCLENDON R.E. HAO Y. SHI Q. HJELMELAND A.B. DEWHIRST M.W. RICH J.N. GLIOMA STEM CELLS PROMOTE RADIORESISTANCE BY PREFERENTIAL ACTIVATI… 1113 1151
Knowledge Base 6: KB 6: unlabeled (n = 1331, density =3.32)
KAUFMAN H.L. KOHLHAPP F.J. ZLOZA A. ONCOLYTIC VIRUSES: A NEW CLASS OF IMMUNOTHERAPY DRUGS (2015) 3039 3383
RUSSELL S.J. PENG K.W. BELL J.C. ONCOLYTIC VIROTHERAPY (2012) 1889 2014
LICHTY B.D. BREITBACH C.J. STOJDL D.F. BELL J.C. GOING VIRAL WITH CANCER IMMUNOTHERAPY (2014) 1530 1712
BOMMAREDDY P.K. SHETTIGAR M. KAUFMAN H.L. INTEGRATING ONCOLYTIC VIRUSES IN COMBINATION CANCER IMMUNOTHERAPY (2018) 1201 1370
KELLY E. RUSSELL S.J. HISTORY OF ONCOLYTIC VIRUSES: GENESIS TO GENETIC ENGINEERING (2007) 1200 1271
ANDTBACKA R.H. KAUFMAN H.L. COLLICHIO F. AMATRUDA T. SENZER N. CHESNEY J. DELMAN K.A. AGARWALA S.S. TALIMOGENE LAHERPAREPVEC IMPROVES DURABLE RESPO… 1102 1182
RIBAS A. DUMMER R. PUZANOV I. VANDERWALDE A. ANDTBACKA R.H.I. MICHIELIN O. OLSZANSKI A.J. FERNANDEZ E. ONCOLYTIC VIROTHERAPY PROMOTES INTRATUMORAL … 998 1095
ZAMARIN D. HOLMGAARD R.B. SUBUDHI S.K. PARK J.S. MANSOUR M. PALESE P. MERGHOUB T. ALLISON J.P. LOCALIZED ONCOLYTIC VIROTHERAPY OVERCOMES SYSTEMIC T… 888 954
KAUFMAN H.L. KIM D.W. DERAFFELE G. MITCHAM J. COFFIN R.S. KIM-SCHULZE S. LOCAL AND DISTANT IMMUNITY INDUCED BY INTRALESIONAL VACCINATION WITH AN ON… 765 798
LIU Z. RAVINDRANATHAN R. KALINSKI P. GUO Z.S. BARTLETT D.L. RATIONAL COMBINATION OF ONCOLYTIC VACCINIA VIRUS AND PD-L1 BLOCKADE WORKS SYNERGISTICAL… 732 823

Development of Knowledge Bases

Technical description

In a co-cittion network, the strength of the relationship between a reference pair \(m\) and \(n\) (\(s_{m,n}^{coc}\)) is expressed by the number of publications \(C\) which are jointly citing reference \(m\) and \(n\).

\[s_{m,n}^{coc} = \sum_i c_{i,m} c_{i,n}\]

The intuition here is that references which are frequently cited together are likely to share commonalities in theory, topic, methodology, or context. It can be interpreted as a measure of similarity as evaluated by other researchers that decide to jointly cite both references. Because the publication process is time-consuming, co-citation is a backward-looking measure, which is appropriate to map the relationship between core literature of a field.

Research Areas: Bibliographic coupling analysis

Research Areas main summary

This is arguably the more interesting part. Here, we identify the literature’s current knowledge frontier by carrying out a bibliographic coupling analysis of the publications in our corpus. This measure uses bibliographical information of publications to establish a similarity relationship between them. Again, method details to be found in the tab Technical description. As you will see, we identify the main research area, but also a set of adjacent research areas with some theoretical/methodological/application overlap.

To identify communities in the field’s knowledge frontier (labeled research areas) we again use the Lovain Algorithm (Blondel et al., 2008). We identify the following communities = research areas.

label AU PY TI dgr_int TC TC_year
Research Area 1: RA 1: unlabeled (n = 1782, density =0.26)
RA 1: unlabeled VAN NIEL G;D’ANGELO G;… 2018 SHEDDING LIGHT ON THE CELL BIOLOGY OF EXTRACELLULAR VESICLES 10.77 2601 650.25
RA 1: unlabeled BECKER A;THAKUR BK;WEI… 2016 EXTRACELLULAR VESICLES IN CANCER: CELL-TO-CELL MEDIATORS OF METASTASIS 19.93 910 151.67
RA 1: unlabeled KALLURI R 2016 THE BIOLOGY AND FUNCTION OF EXOSOMES IN CANCER 18.12 899 149.83
RA 1: unlabeled HESSVIK NP;LLORENTE A 2018 CURRENT KNOWLEDGE ON EXOSOME BIOGENESIS AND RELEASE 14.28 1018 254.50
RA 1: unlabeled KALLURI R;LEBLEU VS 2020 THE BIOLOGY, FUNCTION, AND BIOMEDICAL APPLICATIONS OF EXOSOMES 7.88 1596 798.00
RA 1: unlabeled KOWAL J;ARRAS G;COLOMB… 2016 PROTEOMIC COMPARISON DEFINES NOVEL MARKERS TO CHARACTERIZE HETEROGENEOUS POPULATIONS OF EXTRACELLULAR VESICLE SUBTYPES 6.84 1696 282.67
RA 1: unlabeled LI P;KASLAN M;LEE SH;Y… 2017 PROGRESS IN EXOSOME ISOLATION TECHNIQUES 12.15 828 165.60
RA 1: unlabeled MATHIEU M;MARTIN-JAULA… 2019 SPECIFICITIES OF SECRETION AND UPTAKE OF EXOSOMES AND OTHER EXTRACELLULAR VESICLES FOR CELL-TO-CELL COMMUNICATION 7.43 1262 420.67
RA 1: unlabeled WHITESIDE TL 2016 TUMOR-DERIVED EXOSOMES AND THEIR ROLE IN CANCER PROGRESSION 20.74 372 62.00
RA 1: unlabeled ABELS ER;BREAKEFIELD XO 2016 INTRODUCTION TO EXTRACELLULAR VESICLES: BIOGENESIS, RNA CARGO SELECTION, CONTENT, RELEASE, AND UPTAKE 9.58 668 111.33
Research Area 2: RA 2: unlabeled (n = 1319, density =0.12)
RA 2: unlabeled MANTOVANI A;MARCHESI F… 2017 TUMOUR-ASSOCIATED MACROPHAGES AS TREATMENT TARGETS IN ONCOLOGY 2.17 1598 319.60
RA 2: unlabeled FARKONA S;DIAMANDIS EP… 2016 CANCER IMMUNOTHERAPY: THE BEGINNING OF THE END OF CANCER? 3.96 628 104.67
RA 2: unlabeled HARATANI K;HAYASHI H;C… 2018 ASSOCIATION OF IMMUNE-RELATED ADVERSE EVENTS WITH NIVOLUMAB EFFICACY IN NON-SMALL CELL LUNG CANCER 4.63 513 128.25
RA 2: unlabeled SUN C;MEZZADRA R;SCHUM… 2018 REGULATION AND FUNCTION OF THE PD-L1 CHECKPOINT 2.77 758 189.50
RA 2: unlabeled HE C;DUAN X;GUO N;CHAN… 2016 CORE-SHELL NANOSCALE COORDINATION POLYMERS COMBINE CHEMOTHERAPY AND PHOTODYNAMIC THERAPY TO POTENTIATE CHECKPOINT BLOCKADE… 3.50 472 78.67
RA 2: unlabeled PFIRSCHKE C;ENGBLOM C;… 2016 IMMUNOGENIC CHEMOTHERAPY SENSITIZES TUMORS TO CHECKPOINT BLOCKADE THERAPY 2.81 524 87.33
RA 2: unlabeled HUGO W;ZARETSKY JM;SUN… 2016 GENOMIC AND TRANSCRIPTOMIC FEATURES OF RESPONSE TO ANTI-PD-1 THERAPY IN METASTATIC MELANOMA 0.97 1482 247.00
RA 2: unlabeled SMYTH MJ;NGIOW SF;RIBA… 2016 COMBINATION CANCER IMMUNOTHERAPIES TAILORED TO THE TUMOUR MICROENVIRONMENT 2.47 556 92.67
RA 2: unlabeled YANG W;BAI Y;XIONG Y;Z… 2016 POTENTIATING THE ANTITUMOUR RESPONSE OF CD8+ T CELLS BY MODULATING CHOLESTEROL METABOLISM 2.78 388 64.67
RA 2: unlabeled ADVANI R;FLINN I;POPPL… 2018 CD47 BLOCKADE BY HU5F9-G4 AND RITUXIMAB IN NON-HODGKIN’S LYMPHOMA 2.34 456 114.00
Research Area 3: RA 3: unlabeled (n = 1032, density =0.97)
RA 3: unlabeled GEIGER R;RIECKMANN JC;… 2016 L-ARGININE MODULATES T CELL METABOLISM AND ENHANCES SURVIVAL AND ANTI-TUMOR ACTIVITY 12.42 622 103.67
RA 3: unlabeled GEYER PE;KULAK NA;PICH… 2016 PLASMA PROTEOME PROFILING TO ASSESS HUMAN HEALTH AND DISEASE 18.44 334 55.67
RA 3: unlabeled MEIER F;BRUNNER A-D;KO… 2018 ONLINE PARALLEL ACCUMULATION–SERIAL FRAGMENTATION (PASEF) WITH A NOVEL TRAPPED ION MOBILITY MASS SPECTROMETER 16.00 253 63.25
RA 3: unlabeled WHITHAM M;PARKER BL;FR… 2018 EXTRACELLULAR VESICLES PROVIDE A MEANS FOR TISSUE CROSSTALK DURING EXERCISE 15.54 249 62.25
RA 3: unlabeled STEGER M;DIEZ F;DHEKNE… 2017 SYSTEMATIC PROTEOMIC ANALYSIS OF LRRK2-MEDIATED RAB GTPASE PHOSPHORYLATION ESTABLISHES A CONNECTION TO CILIOGENESIS 18.79 184 36.80
RA 3: unlabeled ITZHAK DN;TYANOVA S;CO… 2016 GLOBAL, QUANTITATIVE AND DYNAMIC MAPPING OF PROTEIN SUBCELLULAR LOCALIZATION 11.41 272 45.33
RA 3: unlabeled SCHROEDER BO;BIRCHENOU… 2018 BIFIDOBACTERIA OR FIBER PROTECTS AGAINST DIET-INDUCED MICROBIOTA-MEDIATED COLONIC MUCUS DETERIORATION 10.42 287 71.75
RA 3: unlabeled GRASSL N;KULAK NA;PICH… 2016 ULTRA-DEEP AND QUANTITATIVE SALIVA PROTEOME REVEALS DYNAMICS OF THE ORAL MICROBIOME 23.44 122 20.33
RA 3: unlabeled BACHE N;GEYER PE;BEKKE… 2018 A NOVEL LC SYSTEM EMBEDS ANALYTES IN PRE-FORMED GRADIENTS FOR RAPID, ULTRA-ROBUST PROTEOMICS 23.93 110 27.50
RA 3: unlabeled BONFIGLIO JJ;FONTANA P… 2017 SERINE ADP-RIBOSYLATION DEPENDS ON HPF1 16.24 157 31.40
Research Area 4: RA 4: unlabeled (n = 969, density =0.22)
RA 4: unlabeled TYANOVA S;TEMU T;COX J 2016 THE MAXQUANT COMPUTATIONAL PLATFORM FOR MASS SPECTROMETRY-BASED SHOTGUN PROTEOMICS 8.34 1502 250.33
RA 4: unlabeled TYANOVA S;TEMU T;SINIT… 2016 THE PERSEUS COMPUTATIONAL PLATFORM FOR COMPREHENSIVE ANALYSIS OF (PROTE)OMICS DATA 3.16 2960 493.33
RA 4: unlabeled AEBERSOLD R;MANN M 2016 MASS-SPECTROMETRIC EXPLORATION OF PROTEOME STRUCTURE AND FUNCTION 7.56 988 164.67
RA 4: unlabeled VIZCAÍNO JA;CSORDAS A;… 2016 2016 UPDATE OF THE PRIDE DATABASE AND ITS RELATED TOOLS 1.73 2696 449.33
RA 4: unlabeled LUDWIG C;GILLET L;ROSE… 2018 DATA-INDEPENDENT ACQUISITION-BASED SWATH-MS FOR QUANTITATIVE PROTEOMICS: A TUTORIAL 6.65 353 88.25
RA 4: unlabeled KLAEGER S;HEINZLMEIR S… 2017 THE TARGET LANDSCAPE OF CLINICAL KINASE DRUGS 5.68 370 74.00
RA 4: unlabeled HOLDT LM;STAHRINGER A;… 2016 CIRCULAR NON-CODING RNA ANRIL MODULATES RIBOSOMAL RNA MATURATION AND ATHEROSCLEROSIS IN HUMANS 3.14 614 102.33
RA 4: unlabeled MEIER F;GEYER PE;VIRRE… 2018 BOXCAR ACQUISITION METHOD ENABLES SINGLE-SHOT PROTEOMICS AT A DEPTH OF 10,000 PROTEINS IN 100 MINUTES 6.06 191 47.75
RA 4: unlabeled GEYER PE;HOLDT LM;TEUP… 2017 REVISITING BIOMARKER DISCOVERY BY PLASMA PROTEOMICS 3.61 318 63.60
RA 4: unlabeled RIECKMANN JC;GEIGER R;… 2017 SOCIAL NETWORK ARCHITECTURE OF HUMAN IMMUNE CELLS UNVEILED BY QUANTITATIVE PROTEOMICS 6.55 171 34.20
Research Area 5: RA 5: unlabeled (n = 850, density =0.21)
RA 5: unlabeled LEVY JMM;TOWERS CG;THO… 2017 TARGETING AUTOPHAGY IN CANCER 4.48 1171 234.20
RA 5: unlabeled KIMMELMAN AC;WHITE E 2017 AUTOPHAGY AND TUMOR METABOLISM 7.85 441 88.20
RA 5: unlabeled AMARAVADI R;KIMMELMAN … 2016 RECENT INSIGHTS INTO THE FUNCTION OF AUTOPHAGY IN CANCER 6.93 447 74.50
RA 5: unlabeled DIKIC I;ELAZAR Z 2018 MECHANISM AND MEDICAL IMPLICATIONS OF MAMMALIAN AUTOPHAGY 2.29 1081 270.25
RA 5: unlabeled YUN CW;LEE SH 2018 THE ROLES OF AUTOPHAGY IN CANCER 6.93 332 83.00
RA 5: unlabeled LEVINE B;KROEMER G 2019 BIOLOGICAL FUNCTIONS OF AUTOPHAGY GENES: A DISEASE PERSPECTIVE 2.52 850 283.33
RA 5: unlabeled SINGH SS;VATS S;CHIA A… 2018 DUAL ROLE OF AUTOPHAGY IN HALLMARKS OF CANCER 4.99 273 68.25
RA 5: unlabeled MAUTHE M;ORHON I;ROCCH… 2018 CHLOROQUINE INHIBITS AUTOPHAGIC FLUX BY DECREASING AUTOPHAGOSOME-LYSOSOME FUSION 1.60 758 189.50
RA 5: unlabeled AMARAVADI RK;KIMMELMAN… 2019 TARGETING AUTOPHAGY IN CANCER: RECENT ADVANCES AND FUTURE DIRECTIONS 4.17 290 96.67
RA 5: unlabeled ONORATI AV;DYCZYNSKI M… 2018 TARGETING AUTOPHAGY IN CANCER 4.25 260 65.00
Research Area 6: RA 6: unlabeled (n = 761, density =0.21)
RA 6: unlabeled WANG J;CAZZATO E;LADEW… 2016 CLONAL EVOLUTION OF GLIOBLASTOMA UNDER THERAPY 5.49 373 62.17
RA 6: unlabeled LAN X;JÖRG DJ;CAVALLI … 2017 FATE MAPPING OF HUMAN GLIOBLASTOMA REVEALS AN INVARIANT STEM CELL HIERARCHY 5.26 185 37.00
RA 6: unlabeled SIDDIQUI I;SCHAEUBLE K… 2019 INTRATUMORAL TCF1 + PD-1 + CD8 + T CELLS WITH STEM-LIKE PROPERTIES PROMOTE TUMOR CONTROL IN RESPONSE TO VACCINATION AND CH… 2.16 435 145.00
RA 6: unlabeled BARTHEL FP;JOHNSON KC;… 2019 LONGITUDINAL MOLECULAR TRAJECTORIES OF DIFFUSE GLIOMA IN ADULTS 5.91 136 45.33
RA 6: unlabeled REIFENBERGER G;WIRSCHI… 2017 ADVANCES IN THE MOLECULAR GENETICS OF GLIOMAS-IMPLICATIONS FOR CLASSIFICATION AND THERAPY 2.51 310 62.00
RA 6: unlabeled CAPPER D;JONES DTW;SIL… 2018 DNA METHYLATION-BASED CLASSIFICATION OF CENTRAL NERVOUS SYSTEM TUMOURS 0.78 976 244.00
RA 6: unlabeled OSUKA S;VAN MEIR EG 2017 OVERCOMING THERAPEUTIC RESISTANCE IN GLIOBLASTOMA: THE WAY FORWARD 3.39 223 44.60
RA 6: unlabeled ZHAO J;CHEN AX;GARTREL… 2019 IMMUNE AND GENOMIC CORRELATES OF RESPONSE TO ANTI-PD-1 IMMUNOTHERAPY IN GLIOBLASTOMA 2.44 302 100.67
RA 6: unlabeled LEE J-K;WANG J;SA JK;L… 2017 SPATIOTEMPORAL GENOMIC ARCHITECTURE INFORMS PRECISION ONCOLOGY IN GLIOBLASTOMA 4.80 135 27.00
RA 6: unlabeled MILLER AM;SHAH RH;PENT… 2019 TRACKING TUMOUR EVOLUTION IN GLIOMA THROUGH LIQUID BIOPSIES OF CEREBROSPINAL FLUID 3.35 192 64.00
Research Area 7: RA 7: unlabeled (n = 690, density =0.19)
RA 7: unlabeled DONGRE A;WEINBERG RA 2019 NEW INSIGHTS INTO THE MECHANISMS OF EPITHELIAL–MESENCHYMAL TRANSITION AND IMPLICATIONS FOR CANCER 3.64 1096 365.33
RA 7: unlabeled BRABLETZ T;KALLURI R;N… 2018 EMT IN CANCER 3.38 947 236.75
RA 7: unlabeled PURAM SV;TIROSH I;PARI… 2017 SINGLE-CELL TRANSCRIPTOMIC ANALYSIS OF PRIMARY AND METASTATIC TUMOR ECOSYSTEMS IN HEAD AND NECK CANCER 2.89 801 160.20
RA 7: unlabeled CHAFFER CL;SAN JUAN BP… 2016 EMT, CELL PLASTICITY AND METASTASIS 4.57 455 75.83
RA 7: unlabeled BATLLE E;CLEVERS H 2017 CANCER STEM CELLS REVISITED 1.70 1104 220.80
RA 7: unlabeled MITTAL V 2018 EPITHELIAL MESENCHYMAL TRANSITION IN TUMOR METASTASIS 4.31 420 105.00
RA 7: unlabeled PASTUSHENKO I;BRISEBAR… 2018 IDENTIFICATION OF THE TUMOUR TRANSITION STATES OCCURRING DURING EMT 2.54 621 155.25
RA 7: unlabeled YANG J;ANTIN P;BERX G;… 2020 GUIDELINES AND DEFINITIONS FOR RESEARCH ON EPITHELIAL–MESENCHYMAL TRANSITION 3.21 450 225.00
RA 7: unlabeled AIELLO NM;MADDIPATI R;… 2018 EMT SUBTYPE INFLUENCES EPITHELIAL PLASTICITY AND MODE OF CELL MIGRATION 4.70 267 66.75
RA 7: unlabeled KREBS AM;MITSCHKE J;LO… 2017 THE EMT-ACTIVATOR ZEB1 IS A KEY FACTOR FOR CELL PLASTICITY AND PROMOTES METASTASIS IN PANCREATIC CANCER 2.31 503 100.60
Research Area 8: RA 8: unlabeled (n = 564, density =0.3)
RA 8: unlabeled CECCARELLI M;BARTHEL F… 2016 MOLECULAR PROFILING REVEALS BIOLOGICALLY DISCRETE SUBSETS AND PATHWAYS OF PROGRESSION IN DIFFUSE GLIOMA 4.38 1091 181.83
RA 8: unlabeled WANG Q;HU B;HU X;KIM H… 2017 TUMOR EVOLUTION OF GLIOMA-INTRINSIC GENE EXPRESSION SUBTYPES ASSOCIATES WITH IMMUNOLOGICAL CHANGES IN THE MICROENVIRONMENT 6.15 640 128.00
RA 8: unlabeled LIU J;LICHTENBERG T;HO… 2018 AN INTEGRATED TCGA PAN-CANCER CLINICAL DATA RESOURCE TO DRIVE HIGH-QUALITY SURVIVAL OUTCOME ANALYTICS 2.94 982 245.50
RA 8: unlabeled NEFTEL C;LAFFY J;FILBI… 2019 AN INTEGRATIVE MODEL OF CELLULAR STATES, PLASTICITY, AND GENETICS FOR GLIOBLASTOMA 5.35 515 171.67
RA 8: unlabeled SEGERMAN A;NIKLASSON M… 2016 CLONAL VARIATION IN DRUG AND RADIATION RESPONSE AMONG GLIOMA-INITIATING CELLS IS LINKED TO PRONEURAL-MESENCHYMAL TRANSITION 8.03 105 17.50
RA 8: unlabeled HU B;WANG Q;WANG YA;HU… 2016 EPIGENETIC ACTIVATION OF WNT5A DRIVES GLIOBLASTOMA STEM CELL DIFFERENTIATION AND INVASIVE GROWTH 5.35 147 24.50
RA 8: unlabeled DARMANIS S;SLOAN SA;CR… 2017 SINGLE-CELL RNA-SEQ ANALYSIS OF INFILTRATING NEOPLASTIC CELLS AT THE MIGRATING FRONT OF HUMAN GLIOBLASTOMA 2.10 331 66.20
RA 8: unlabeled BOWMAN RL;KLEMM F;AKKA… 2016 MACROPHAGE ONTOGENY UNDERLIES DIFFERENCES IN TUMOR-SPECIFIC EDUCATION IN BRAIN MALIGNANCIES 2.43 274 45.67
RA 8: unlabeled JACOB F;SALINAS RD;ZHA… 2020 A PATIENT-DERIVED GLIOBLASTOMA ORGANOID MODEL AND BIOBANK RECAPITULATES INTER- AND INTRA-TUMORAL HETEROGENEITY 2.94 221 110.50
RA 8: unlabeled LENTING K;VERHAAK R;TE… 2017 GLIOMA: EXPERIMENTAL MODELS AND REALITY 3.71 159 31.80

Development

Connectivity between the research areas

Technical description

In a bibliographic coupling network, the coupling-strength between publications is determined by the number of commonly cited references they share, assuming a common pool of references to indicate similarity in context, methods, or theory. Formally, the strength of the relationship between a publication pair \(i\) and \(j\) (\(s_{i,j}^{bib}\)) is expressed by the number of commonly cited references.

\[s_{i,j}^{bib} = \sum_m c_{i,m} c_{j,m}\]

Since our corpus contains publications which differ strongly in terms of the number of cited references, we normalize the coupling strength by the Jaccard similarity coefficient. Here, we weight the intercept of two publications’ bibliography (shared refeences) by their union (number of all references cited by either \(i\) or \(j\)). It is bounded between zero and one, where one indicates the two publications to have an identical bibliography, and zero that they do not share any cited reference. Thereby, we prevent publications from having high coupling strength due to a large bibliography (e.g., literature surveys).

\[S_{i,j}^{jac-bib} =\frac{C(i \cap j)}{C(i \cup j)} = \frac{s_{i,j}^{bib}}{c_i + c_j - s_{i,j}^{bib}}\]

More recent articles have a higher pool of possible references to co-cite to, hence they are more likely to be coupled. Consequently, bibliographic coupling represents a forward looking measure, and the method of choice to identify the current knowledge frontier at the point of analysis.

Knowledge Bases, Research Areas & Topics Interaction

Endnotes

All results are preliminary so far…